We describe the Fusion-In-Decoder (FID) model [1]} as an example.
FID is developed for open-domain QA, and is based on a pre-trained sequence-to-sequence model, T5 [2]}. As illustrated in Figure REF , given question \(Q\)  and a set of \(N\)  retrieved passages \(\mathcal {P} = \lbrace P_1,...,P_N\rbrace \) , the encoder, which is similar to what is described in Section REF , encodes each \((Q,P_n)\)  pair independently, and produces an integrated contextual vector representation for each token, \(\mathbf {m}^n_j\)  for the \(j\) -th token of the \(n\) -th pair. The decoder then performs attention over the concatenation of the representations of all the retrieved passages, and generate the answer string.
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